About me
Wearer of many hats! Data Wrangling, Data Visualization, Modelling, Dashboards, Web Development and Research.
Prior to consulting role, studied emerging infectious diseases and spatial epidemiology
Lauren Yee Data Scientist
Wearer of many hats! Data Wrangling, Data Visualization, Modelling, Dashboards, Web Development and Research.
Prior to consulting role, studied emerging infectious diseases and spatial epidemiology
Lauren Yee Data Scientist
Source: Economist https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368
John Snow, 1854
Source: Bill Rankin
Source: reddit.com/r/dataisbeautiful user:TrustLittleBrother
Size: The size of points are varied based on the values or ratios from the data.
Value/Contrast – how light or dark a particular colour is while hue is held constant
Hue is the use of color – used to distinguish between classes or categories (agricultural land vs urban land use) Hues can be combined with different textures or shapes if there are a large number of categories. Typically when printing images that are in black and white.
Saturation – a mixture of grey and a hue, the intensity of color.
Hue, Contrast, Saturation and Colour are important to understand in terms of accessibility and colourblindness.
Source:Goodchild and Rhind 2015
Shape can be used to divide up categories, draw attention to a particular data point or shape.
In ggplot2 we have the option of these shapes:
The New York Times
viridis and RColorbrewerSequential : Great for low to high values go from light colours to darker
Diverging: highlight differences e.g. standard deviation
Qualitative: Used to present categorical data, soil types, type of neighbourhood
Source: Viridis Colour Scales (Green-Blind (Deuteranopia))
Colour with no purpose takes away from the meaning of the data, aka rainbow effects
With all the plotting libraries available in R - how do you choose?
There are some great flowcharts and websites to make the decision easier when stuck:
A collection of R packages that make it easier to work with data.
dplyr is used to manipulate, group, and summarize data
ggplot2 is a data visualization library that we will be using in the course!
readr loads data into R
tibble data format, similar to data frames
purrr functions which replace for loops with code that is both more succinct and easier to read
tidyr is a library meant for formatting and shaping data into the ‘tidy’ format…
It is often said that 80% of data analysis is spent on the cleaning and preparing data. The goal of tidyr is to help you create tidy data. Tidy data is data where:
“data tidying: structuring datasets to facilitate analysis.”
Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse. It is an attempt to standardize data.
All tidyverse packages are designed to work with tidy data.
All R markdown documents end in .rmd as opposed to .R. The start of a markdown file is called a “YAML”. Here you can specify the title and other meta data attributes to your file, as well as the outputs generated. Such as a pdf, word file, or html document
Each “chunk” represented by ``` of R code can executed independently and visualizations are generated in-line.
eval = FALSE prevents code from being evaluated. This is useful for displaying example code, or for disabling a large block of code without commenting each line.
include = FALSE runs the code, but doesn’t show the code or results in the final document.
echo = FALSE prevents code, but not the results from appearing in the finished file. Use this when writing reports aimed at people who don’t want to see the underlying R code. Or to show a figure generated by ggplot2
